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Finally Getting the Most out of the Java Thread Pool | @CloudExpo #JVM #Java #Cloud
Thread pool is a core concept in multithreaded programming that represents a collection of idle threads used to execute tasks

Finally Getting the Most out of the Java Thread Pool
By Eugen Paraschiv

First, let's outline a frame of reference for multithreading and why we may need to use a thread pool.

A thread is an execution context that can run a set of instructions within a process - aka a running program. Multithreaded programming refers to using threads to execute multiple tasks concurrently. Of course, this paradigm is well supported on the JVM.

Although this brings several advantages, primarily regarding the performance of a program, multithreaded programming can also have disadvantages - such as increased complexity of the code, concurrency issues, unexpected results and adding the overhead of thread creation.

In this article, we're going to take a closer look at how the latter issue can be mitigated by using thread pools in Java.

Why Use a Thread Pool?
Creating and starting a thread can be an expensive process. By repeating this process every time we need to execute a task, we're incurring a significant performance cost - which is exactly what we were attempting to improve by using threads.

For a better understanding of the cost of creating and starting a thread, let's see what the JVM actually does behind the scenes:

  • It allocates memory for a thread stack that holds a frame for every thread method invocation
  • Each frame consists of a local variable array, return value, operand stack and constant pool
  • Some JVMs that support native methods also allocate a native stack
  • Each thread gets a program counter that tells it what the current instruction executed by the processor is
  • The system creates a native thread corresponding to the Java thread
  • Descriptors relating to the thread are added to the JVM internal data structures
  • The threads share the heap and method area

Of course, the details of all this will depend on the JMV and the operating system.

In addition, more threads mean more work for the system scheduler to decide which thread gets access to resources next.

A thread pool helps mitigate the issue of performance by reducing the number of threads needed and managing their lifecycle.
Essentially, threads are kept in the thread pool until they're needed, after which they execute the task and return the pool to be reused later. This mechanism is especially helpful in systems that execute a large number of small tasks.

Java Thread Pools
Java provides its own implementations of the thread pool pattern, through objects called executors. These can be used through executor interfaces or directly through thread pool implementations - which does allow for finer-grained control.

The java.util.concurrent package contains the following interfaces:

  • Executor - a simple interface for executing tasks
  • ExecutorService - a more complex interface which contains additional methods for managing the tasks and the executor itself
  • ScheduledExecutorService - extends ExecutorService with methods for scheduling the execution of a task

Alongside these interfaces, the package also provides the Executors helper class for obtaining executor instances, as well as implementations for these interfaces.

Generally, a Java thread pool is composed of:

  • The pool of worker threads, responsible for managing the threads
  • A thread factory that is responsible for creating new threads
  • A queue of tasks waiting to be executed

In the following sections, let's see how the Java classes and interfaces that provide support for thread pools work in more detail.

The Executors class and Executor interface
The Executors class contains factory methods for creating different types of thread pools, while Executor is the simplest thread pool interface, with a single execute() method.

Let's use these two classes in conjunction with an example that creates a single-thread pool, then uses it to execute a simple statement:

Executor executor = Executors.newSingleThreadExecutor();
executor.execute(() -> System.out.println("Single thread pool test"));

Notice how the statement can be written as a lambda expression - which is inferred to be of Runnable type.

The execute() method runs the statement if a worker thread is available, or places the Runnable task in a queue to wait for a thread to become available.

Basically, the executor replaces the explicit creation and management of a thread.

The factory methods in the Executors class can create several types of thread pools:

  • newSingleThreadExecutor() - a thread pool with only one thread with an unbounded queue, which only executes one task at a time
  • newFixedThreadPool() - a thread pool with a fixed number of threads which share an unbounded queue; if all threads are active when a new task is submitted, they will wait in queue until a thread becomes available
  • newCachedThreadPool() - a thread pool that creates new threads as they are needed
  • newWorkStealingThreadPool() - a thread pool based on a "work-stealing" algorithm which will be detailed more in a later section

Next, let's take a look into what additional capabilities the ExecutorService interface.

The ExecutorService
One way to create an ExecutorService is to use the factory methods from the Executors class:

ExecutorService executor = Executors.newFixedThreadPool(10);

Besides the execute() method, this interface also defines a similar submit() method that can return a Future object:

Callable<Double> callableTask = () -> {
return employeeService.calculateBonus(employee);
};
Future<Double> future = executor.submit(callableTask);
// execute other operations
try {
if (future.isDone()) {
double result = future.get();
}
} catch (InterruptedException | ExecutionException e) {
e.printStackTrace();
}

As you can see in the example above, the Future interface can return the result of a task for Callable objects, and can also show the status of a task execution.

The ExecutorService is not automatically destroyed when there are no tasks waiting to be executed, so to shut it down explicitly, you can use the shutdown() or shutdownNow() APIs:

executor.shutdown();

The ScheduledExecutorService
This is a subinterface of ExecutorService - which adds methods for scheduling tasks:

ScheduledExecutorService executor = Executors.newScheduledThreadPool(10);

The schedule() method specifies a task to be executed, a delay value and a TimeUnit for the value:

Future<Double> future = executor.schedule(callableTask, 2, TimeUnit.MILLISECONDS);

Furthermore, the interface defines two additional methods:

executor.scheduleAtFixedRate(
() -> System.out.println("Fixed Rate Scheduled"), 2, 2000, TimeUnit.MILLISECONDS);

executor.scheduleWithFixedDelay(
() -> System.out.println("Fixed Delay Scheduled"), 2, 2000, TimeUnit.MILLISECONDS);

The scheduleAtFixedRate() method executes the task after 2 ms delay, then repeats it at every 2 seconds. Similarly, the scheduleWithFixedDelay() method starts the first execution after 2 ms, then repeats the task 2 seconds after the previous execution ends.

In the following sections, let's also go through two implementations of the ExecutorService interface: ThreadPoolExecutor and ForkJoinPool.

The ThreadPoolExecutor
This thread pool implementation adds the ability to configure parameters
, as well as extensibility hooks. The most convenient way to create a ThreadPoolExecutor object is by using the Executors factory methods:

ThreadPoolExecutor executor = (ThreadPoolExecutor) Executors.newFixedThreadPool(10);

In this manner, the thread pool is preconfigured for the most common cases. The number of threads can be controlled by setting the parameters:

  • corePoolSize and maximumPoolSize - which represent the bounds of the number of threads
  • keepAliveTime - which determines the time to keep extra threads alive

Digging a bit further, here's how these parameters are used.

If a task is submitted and fewer than corePoolSize threads are in execution, then a new thread is created. The same thing happens if there are more than corePoolSize but less than maximumPoolSize threads running, and the task queue is full. If there are more than corePoolSize threads which have been idle for longer than keepAliveTime, they will be terminated.

In the example above, the newFixedThreadPool() method creates a thread pool with corePoolSize=maximumPoolSize=10, and a keepAliveTime of 0 seconds.

If you use the newCachedThreadPool() method instead, this will create a thread pool with a maximumPoolSize of Integer.MAX_VALUE and a keepAliveTime of 60 seconds:

ThreadPoolExecutor cachedPoolExecutor
= (ThreadPoolExecutor) Executors.newCachedThreadPool();

The parameters can also be set through a constructor or through setter methods:

ThreadPoolExecutor executor = new ThreadPoolExecutor(
4, 6, 60, TimeUnit.SECONDS, new LinkedBlockingQueue<Runnable>()
);
executor.setMaximumPoolSize(8);

A subclass of ThreadPoolExecutor is the ScheduledThreadPoolExecutor class, which implements the ScheduledExecutorService interface. You can create this type of thread pool by using the newScheduledThreadPool() factory method:

ScheduledThreadPoolExecutor executor
= (ScheduledThreadPoolExecutor) Executors.newScheduledThreadPool(5);

This creates a thread pool with a corePoolSize of 5, an unbounded maximumPoolSize and a keepAliveTime of 0 seconds.

The ForkJoinPool
Another implementation of a thread pool is the ForkJoinPool class. This implements the ExecutorService interface and represents the central component of the fork/join framework introduced in Java 7.

The fork/join framework is based on a "work-stealing algorithm". In simple terms, what this means is that threads that run out of tasks can "steal" work from other busy threads.

A ForkJoinPool is well suited for cases when most tasks create other subtasks or when many small tasks are added to the pool from external clients.

The workflow for using this thread pool typically looks something like this:

  • create a ForkJoinTask subclass
  • split the tasks into subtasks according to a condition
  • invoke the tasks
  • join the results of each task
  • create an instance of the class and add it to the pool

To create a ForkJoinTask, you can choose one of its more commonly used subclasses, RecursiveAction or RecursiveTask - if you need to return a result.

Let's implement an example of a class that extends RecursiveTask and calculates the factorial of a number by splitting it into subtasks depending on a THRESHOLD value:

public class FactorialTask extends RecursiveTask<BigInteger> {
private int start = 1;
private int n;
private static final int THRESHOLD = 20;

// standard constructors

@Override
protected BigInteger compute() {
if ((n - start) >= THRESHOLD) {
return ForkJoinTask.invokeAll(createSubtasks())
.stream()
.map(ForkJoinTask::join)
.reduce(BigInteger.ONE, BigInteger::multiply);
} else {
return calculate(start, n);
}
}
}

The main method that this class needs to implement is the overridden compute() method, which joins the result of each subtask.

The actual splitting is done in the createSubtasks() method:

private Collection<FactorialTask> createSubtasks() {
List<FactorialTask> dividedTasks = new ArrayList<>();
int mid = (start + n) / 2;
dividedTasks.add(new FactorialTask(start, mid));
dividedTasks.add(new FactorialTask(mid + 1, n));
return dividedTasks;
}

Finally, the calculate() method contains the multiplication of values in a range:

private BigInteger calculate(int start, int n) {
return IntStream.rangeClosed(start, n)
.mapToObj(BigInteger::valueOf)
.reduce(BigInteger.ONE, BigInteger::multiply);
}

Next, tasks can be added to a thread pool:

ForkJoinPool pool = ForkJoinPool.commonPool();
BigInteger result = pool.invoke(new FactorialTask(100));

ThreadPoolExecutor vs. ForkJoinPool
At first look, it seems that the fork/join framework brings improved performance. However, this may not always be the case depending on the type of problem you need to solve.

When choosing a thread pool, it's important to also remember there is overhead caused by creating and managing threads and switching execution from one thread to another.

The ThreadPoolExecutor provides more control over the number of threads and the tasks that are executed by each thread. This makes it more suitable for cases when you have a smaller number of larger tasks that are executed on their own threads.

By comparison, the ForkJoinPool is based on threads "stealing" tasks from other threads. Because of this, it is best used to speed up work in cases when tasks can be broken up into smaller tasks.

To implement the work-stealing algorithm, the fork/join framework uses two types of queues:

  • A central queue for all tasks
  • A task queue for each thread

When threads run out of tasks in their own queues, they attempt to take tasks from the other queues. To make the process more efficient, the thread queue uses a deque (double ended queue) data structure, with threads being added at one end and "stolen" from the other end.

Here is a good visual representation of this process from The H Developer:

fork/join thread pool

In contrast with this model, the ThreadPoolExecutor uses only one central queue.

One last thing to remember is that the choosing a ForkJoinPool is only useful if the tasks create subtasks. Otherwise, it will function the same as a ThreadPoolExecutor, but with extra overhead.

Tracing Thread Pool Execution
Now that we have a good foundational understanding of the Java thread pool ecosystem, let's take a closer look at what happens during the execution of an application that uses a thread pool.

By adding some logging statements in the constructor of FactorialTask and the calculate() method, you can follow the invocation sequence:

13:07:33.123 [main] INFO ROOT - New FactorialTask Created
13:07:33.123 [main] INFO ROOT - New FactorialTask Created
13:07:33.123 [main] INFO ROOT - New FactorialTask Created
13:07:33.123 [main] INFO ROOT - New FactorialTask Created 13:07:33.123 [ForkJoinPool.commonPool-worker-1] INFO ROOT - New FactorialTask Created
13:07:33.123 [ForkJoinPool.commonPool-worker-1] INFO ROOT - New FactorialTask Created
13:07:33.123 [main] INFO ROOT - New FactorialTask Created
13:07:33.123 [main] INFO ROOT - New FactorialTask Created
13:07:33.123 [main] INFO ROOT - Calculate factorial from 1 to 13
13:07:33.123 [ForkJoinPool.commonPool-worker-1] INFO ROOT - New FactorialTask Created
13:07:33.123 [ForkJoinPool.commonPool-worker-2] INFO ROOT - New FactorialTask Created
13:07:33.123 [ForkJoinPool.commonPool-worker-1] INFO ROOT - New FactorialTask Created
13:07:33.123 [ForkJoinPool.commonPool-worker-2] INFO ROOT - New FactorialTask Created
13:07:33.123 [ForkJoinPool.commonPool-worker-1] INFO ROOT - Calculate factorial from 51 to 63
13:07:33.123 [ForkJoinPool.commonPool-worker-2] INFO ROOT - Calculate factorial from 76 to 88
13:07:33.123 [ForkJoinPool.commonPool-worker-3] INFO ROOT - Calculate factorial from 64 to 75
13:07:33.163 [ForkJoinPool.commonPool-worker-3] INFO ROOT - New FactorialTask Created
13:07:33.163 [main] INFO ROOT - Calculate factorial from 14 to 25
13:07:33.163 [ForkJoinPool.commonPool-worker-3] INFO ROOT - New FactorialTask Created
13:07:33.163 [ForkJoinPool.commonPool-worker-2] INFO ROOT - Calculate factorial from 89 to 100
13:07:33.163 [ForkJoinPool.commonPool-worker-3] INFO ROOT - Calculate factorial from 26 to 38
13:07:33.163 [ForkJoinPool.commonPool-worker-3] INFO ROOT - Calculate factorial from 39 to 50

Here you can see there are several tasks created, but only 3 worker threads - so these get picked up by the available threads in the pool.

Also notice how the objects themselves are actually created in the main thread, before being passed to the pool for execution.

This is actually a great way to explore and understand thread pools at runtime, with the help of a solid logging visualization tool such as Prefix.

The core aspect of logging from a thread pool is to make sure the thread name is easily identifiable in the log message; Log4J2 is a great way to do that by making good use of layouts for example.

Potential Risks of Using a Thread Pool
Although thread pools provide significant advantages, you can also encounter several problems while using one, such as:

  • Using a thread pool that is too large or too small - if the thread pool contains too many threads, this can significantly affect the performance of the application; on the other hand, a thread pool that is too small may not bring the performance gain that you would expect
  • Deadlock can happen just like in any other multi-threading situation; for example, a task may be waiting for another task to complete, with no available threads for this latter one to execute; that's why it's usually a good idea to avoid dependencies between tasks
  • Queuing a very long task - to avoid blocking a thread for too long, you can specify a maximum wait time after which the task is rejected or re-added to the queue

To mitigate these risks, you have to choose the thread pool type and parameters carefully, according to the tasks that they will handle. Stress-testing your system is also well-worth it to get some real-world data of how your thread pool behaves under load.

Conclusion
Thread pools provide a significant advantage by, simply put, separating the execution of tasks from the creation and management of threads. Additionally, when used right, they can greatly improve the performance of your application.

And, the great thing about the Java ecosystem is that you have access to some of the most mature and battle-tested implementations of thread-pools out there, if you learn to leverage them properly and take full advantage of them.

The post Finally Getting the Most out of the Java Thread Pool appeared first on Stackify.

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An increasing number of companies are creating products that combine data with analytical capabilities. Running interactive queries on Big Data requires complex architectures to store and query data effectively, typically involving data streams, an choosing efficient file format/database and multiple independent systems that are tied together through custom-engineered pipelines. In his session at @BigDataExpo at @ThingsExpo, Tomer Levi, a senior software engineer at Intel’s Advanced Analytics gr...
Everything run by electricity will eventually be connected to the Internet. Get ahead of the Internet of Things revolution. In his session at @ThingsExpo, Akvelon expert and IoT industry leader Sergey Grebnov provided an educational dive into the world of managing your home, workplace and all the devices they contain with the power of machine-based AI and intelligent Bot services for a completely streamlined experience.
As many know, the first generation of Cloud Management Platform (CMP) solutions were designed for managing virtual infrastructure (IaaS) and traditional applications. But that's no longer enough to satisfy evolving and complex business requirements. In his session at 21st Cloud Expo, Scott Davis, Embotics CTO, explored how next-generation CMPs ensure organizations can manage cloud-native and microservice-based application architectures, while also facilitating agile DevOps methodology. He expla...

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WebRTC Summit Silicon Valley All-Star Speakers Include

MATTHIEU
Octoblu

MAHADEV
Cisco

MCCARTHY
Bsquare

FELICIANO
AMDG

PAUL
VenueNext

SMITH
Eviot

BEAMER
goTraverse

GETTENS
goTraverse

CHAMBLISS
ReadyTalk

HERBERTS
Cityzen Data

REITBAUER
Dynatrace

WILLIAM-
SON

Cloud
Computing

SCHMARZO
EMC

WOOD
VeloCloud

WALLGREN
Electric Cloud

VARAN-
NATH

GE

SRIDHARA-
BALAN

Pulzze

METRIC
Linux

MONTES
Iced

ARIOLA
Parasoft

HOLT
Daitan

CUNNING-
HAM

ReadyTalk

BEDRO-
SIAN

Cypress

NAMIE
Cisco

NAKA-
GAWA

Transparent
Cloud

SHIBATA
Transparent
Cloud

BOYD
Neo4j

WARD
DWE

MILLER
Covisint

EVAVOLD
Covisint

MEINER
Oracle

MEEHAN
Esri

WITECK
Citrix

LIANG
Rancher Labs

BUTLER
Tego

ROWE
IBM Cloud

SKILLERN
Intel

SMITH
Numerex
WebRTC Summit New York All-Star Speakers Include

CLELAND
HGST

VASILIOU
Catchpoint

WALLGREN
Electric Cloud

HINCH-
CLIFFE

7Summits

DE SOUZA
Cisco

RANDALL
Gartner

ARM-
STRONG

AppNeta

SMALL-
TREE

Cazena

MCCARTHY
Bsquare

DELOACH
Infobright

QUINT
Ontegrity

MALAU-
CHLAN

Buddy Platform

PALIOTTA
Vector

MITRA
Cognizant

KOCHER
Grey Heron

PAPDO
POULOS

Cloud9

HARLAN
Two Bulls

GOLO
SHUBIN

Bit6

PROIETTI
Location
Smart

MARTIN
nfrastructure

MOULINE
Everbridge

MARSH
Blue Pillar

PARKS
SecureRF

PEROTTI
Plantronics

HOFFMAN
EastBanc

WATSON
Trendalyze

BENSON-
OFF

Unigma

SHAN
CTS

MATTELA
Redpine

GILLEN
Spark
Coginition

SOLT
Netvibes

BERN-
ARDO

GE Digital

ROMAN-
SKY

TrustPoint

BEAMER
GoTransverse

LESTER
LogMeIn

PONO
-MAREVA

Google

SINGH
Sencha

CALKINS
Amadeus

KLEIN
Rachio

HOASIN
Aeris

SARKARIA
PHEMI

SPROULE
Metavine

SNELL
Intel

LEVINE
CytexOne

ALLEN
Freewave

MCCAL-
LUM

Falconstor

HYEDT
Seamless

WebRTC Summit Silicon Valley All-Star Speakers Include

SCHULZ
Luxoft

TAM-
BURINI

Autodesk

MCCARTHY
Bsquare

THURAI
SaneIoT

TURNER
Cloudian

ENDO
Intrepid

NAKAGAWA
Transparent

SHIBATA
Transparent

LEVANT-LEVI
testRTC

VARAN NATH
GE

COOPER
M2Mi

SENAY
Teletax

SKEEN
Vitria

KOCHER
Grey Heron

GREENE
PubNub

MAGUIRE
HP

MATTHIEU
Octoblu

STEINER-
JOVIC

AweSense

LYNN
AgilData

HEDGES
Cloudata

DUFOUR
Webroot

ROBERTS
Platform

JONES
Deep

PFEIFFER
NICTA

NIELSEN
Redis

PAOLAL-
ANTORIO

DataArchon

KAHN
Solgenia

LOPEZ
Kurento

KIM
MapR

BROMHEAD
Instaclustr

LEVINE
CytexOne

BONIFAZI
Solgenia

GORBA-
CHEV

Intelligent
Systems

THYKAT-
TIL

Navisite

TRELOAR
Bebaio

SIVARAMA-
KRISHNAN

Red Hat
Cloud Expo New York All-Star Speakers Included

DE SOUZA
Cisco

POTTER
SafeLogic

ROBINSON
CompTIA

WARUSA
-WITHANA

WSO2 Inc

MEINER
Oracle

CHOU
Microsoft

HARRISON
Tufin

BRUNOZZI
VMware

KIM
MapR

KANE
Dyn

SICULAR
Basho

TURNER
Cloudian

KUMAR
Liaison

ADAMIAK
Liaison

KHAN
Solgenia

BONIFAZI
Solgenia

SUSSMAN
Coalfire

ISAACSON
RMS

LYNN
CodeFutures

HEABERLIN
Windstream

RAMA
MURTHY

Virtusa

BOSTOCK
IndependenceIT

DE MENO
CommVault

GRILLI
Adobe

WILLIAMS
Rancher Labs

CRISWELL
Alert Logic

COTY
Alert Logic

JACOBS
SingleHop

MARAVEI
Cisco

JACKSON
Softlayer

SINGH
IBM

HAZARD
Softlayer

GALLO
Softlayer

TAMASKAR
GENBAND

SUBRA
-MANIAN

Emcien

LEVESQUE
Windstream

IVANOV
StorPool

BLOOM-
BERG

Intellyx

BUDHANI
Soha

HATHAWAY
IBM Watson

TOLL
ProfitBricks

LANDRY
Microsoft

BEARFIELD
Blue Box

HERITAGE
Akana

PILUSO
SIASMSP

HOLT
IBM Cloudant

SHAN
CTS

PICCIN-
INNI

EMC

BRON-
GERSMA

Modulus

PAIGE
CenturyLink

SABHIKHI
Cognitive Scale

MILLS
Green House Data

KATZEN
CenturyLink

SLOPER
CenturyLink

SRINIVAS
EMC

TALREJA
Cisco

GORBACHEV
Systems Services Inc.

COLLISON
Apcera

PRABHU
OpenCrowd

LYNN
CodeFutures

SWARTZ
Ericsson

MOSHENKO
CoreOS

BERMING-
HAM

SIOS

WILLIS
Stateless Networks

MURPHY
Gridstore

KHABE
Vicom

NIKOLOV
GetClouder

DIETZE
Windstream

DALRY-
MPLE

EnterpriseDB

MAZZUCCO
TierPoint

RIVERA
WHOA.com

HERITAGE
Akana

SEYMOUR
6fusion

GIANNETTO
Author

CARTER
IBM

ROGERS
Virtustream
Cloud Expo Silicon Valley All-Star Speakers

TESAR
Microsoft

MICKOS
HP

BHARGAVA
Intel

RILEY
Riverbed

DEVINE
IBM

ISAACSON
CodeFutures

LYNN
HP

HINKLE
Citrix

KHAN
Solgenia

SINGH
Bigdata

BEACH
SendGrid

BOSTOCK
IndependenceIT

DE SOUZA
Cisco

PATTATHIL
Harbinger

O'BRIEN
Aria Systems

BONIFAZI
Solgenia

BIANCO
Solgenia

PROCTOR
NuoDB

DUGGAL
EnterpriseWeb

TEGETHOFF
Appcore

BRUNOZZI
VMware

HICKENS
Parasoft

KLEBANOV
Cisco

PETERS
Esri

GOLDBERG
Vormetric

CUMBER-
LAND

Dimension

ROSENDAHL
Quantum

LOOMIS
Cloudant

BRUNO
StackIQ

HANNON
SoftLayer

JACKSON
SoftLayer

HOCH
Virtustream

KAPADIA
Seagate

PAQUIN
OnLive

TSAI
Innodisk

BARRALL
Connected Data

SHIAH
AgilePoint

SEGIL
Verizon

PODURI
Citrix

COWIE
Dyn

RITTEN-
HOUSE

Cisco

FALLOWS
Kaazing

THYKATTIL
TimeWarner

LEIDUCK
SAP

LYNN
HP

WAGSTAFF
BSQUARE

POLLACK
AOL

KAMARAJU
Vormetric

BARRY
Catbird

MENDEN-
HALL

SUPERNAP

SHAN
KEANE

PLESE
Verizon

BARNUM
Voxox

TURNER
Cloudian

CALDERON
Advanced Systems

AGARWAL
SOA Software

LEE
Quantum

OBEROI
Concurrent, Inc.

HATEM
Verizon

GALEY
Autodesk

CAUTHRON
NIMBOXX

BARSOUM
IBM

GORDON
1Plug

LEWIS
Verizon

YEO
OrionVM

NAKAGAWA
Transparent Cloud Computing

SHIBATA
Transparent Cloud Computing

NATH
GE

GOKCEN
GE

STOICA
Databricks

TANKEL
Pivotal Software


Testimonials
This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
@SteveMar_Msft
General Manager of Window Azure
 
How does Cloud Expo do it every year? Another INCREDIBLE show - our heads are spinning - so fun and informative."
@SOASoftwareInc
 
Thank you @ThingsExpo for such a great event. All of the people we met over the past three days makes us confident IoT has a bright future."
Yasser Khan
CEO of @Cnnct2me
 
One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

@Peak_Ten


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@CloudExpo Blogs
Blockchain. A day doesn’t seem to go by without seeing articles and discussions about the technology. According to PwC executive Seamus Cushley, approximately $1.4B has been invested in blockchain just last year. In Gartner’s recent hype cycle for emerging technologies, blockchain is approaching the peak. It is considered by Gartner as one of the ‘Key platform-enabling technologies to track.’ While there is a lot of ‘hype vs reality’ discussions going on, there is no arguing that blockchain is being taken very seriously across industries and cannot be ignored.
As you have probably heard, the EU commission signed the General Data Protection Regulation (GDPR) back in April 2016. The legislation is designed to help companies handle efficiently the data challenges of the 21st century and give strict guidelines as to how to work with massive flows of digital information. It is set to protect web users (data subjects) from malicious use and loss of their personal info and, also, to give people greater control over how their records are processed.
2017 was the year of ransomware. Ransomware has been around for years, but the victims were typically non-technical consumers: the impact, although serious from the victim's perspective, was limited. In 2017 we've seen huge ransomware attacks close down hospitals and businesses, putting lives and billions of dollars at risk.
It’s conference season and, as you might expect, Jason and I have been on the road covering a bunch of them. It’s always great to see what the disruptive players in the market are doing — and this year did not disappoint. But there is one thing that repeatedly happens that just gets under my skin: transformation-washing. As Jason explained in a Forbes article over a year ago, ‘washing’ is when a vendor (or pundit) applies a buzzword loosely in an overt attempt to attach themselves to its buzz. And transformation-washing is rampant.
We’re seeing an emerging trend in the cloud computing world. I’ve been referring to it as cloud fatigue, but it’s more commonly known as repatriation, or moving workloads from the cloud back to on-prem locations. According to a recent 451 Research report, over 21 percent of organizations have plans to pull back from the cloud and return to an on-prem infrastructure in 2017. Considering the vast growth of cloud adoption over the last several years, what’s behind this trend?
How is DevOps going within your organization? If you need some help measuring just how well it is going, we have prepared a list of some key DevOps metrics to track. These metrics can help you understand how your team is doing over time. The word DevOps means different things to different people. Some say it a culture and every vendor in the industry claims that their tools help with DevOps. Depending on how you define DevOps, some of these metrics may matter more or less to you and your team.
The word polymorphism is used in various contexts and describes situations in which something occurs in several different forms. In computer science, it describes the concept that objects of different types can be accessed through the same interface. Each type can provide its own, independent implementation of this interface. It is one of the core concepts of object-oriented programming (OOP).
The hotel and hospitality industry, enabled with advanced technology and more collaboration with associated businesses, will see some important trends in 2018 as hotel brands reinvent themselves to cater to a new type of clientele. Millennial guests will dominate the landscape, and reshape the industry with demands for more automated options and conveniences and the ability to do everything from a smartphone, and hotels - eager to deliver more conveniences to this younger audience - will forge closer alliances with retailers and community destination
The cloud market is growing at a rate of 30% annually and is expected to reach $130 billion. Analysts predict that service providers are well positioned to be the leading point of distribution for cloud services in light of the scale of their operations and their capacity to offer end-to-end lifecycle management for IaaS, SaaS and PaaS over secure managed networks.
Our cities have been connected since the dawn of urbanization in the Indus Valley and on the plains of Mesopotamia nearly ten millennia ago. Cities exist to gather and connect people, bringing us together into communities and joint ventures that need complex networks of communication. But in recent years the connected city has come to mean something more. Today and in the future, the connected city will not just be about people connecting with people, but people with machines, people with people via machines, and perhaps most importantly, machines with machines.
Decentralization of everything, the great new idea of which the web can’t stop babbling, might still seem a bit utopian if you inspect it closely. Yes, blockchains are likely to reshape our economy, or a huge part of it, and benefit considerably those who are currently unbanked. They might also facilitate the creation of rating/reputation systems that are not controlled by any single entity and thus allow people (say Uber drivers who’d like to work for Lyft) to switch employers without having to establish their credibility anew. They might give users complete control over their assets; prot...
Quantum Computing is becoming quite the hot topic lately. With research being done by Google, IBM, Microsoft, universities, and a number of other players, it’s looking this is really going to happen. In fact, Google may just be weeks away from announcing the Quantum Supremacy milestone. If you aren’t familiar with the concept of Quantum Supremacy yet, it’s basically the point where a quantum computer can complete a computation in a short time where a classical computer can’t complete it at all. This is a big deal. While there are some simple quantum computers out there right now (you can ...
This phrase is new and it originated at Netflix back in 2010. I was listening to Nora Jones, a Netflix engineer at the AWS re-Invent conference few weeks back, where she talked about this. The principle of Chaos goes like this, “Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.” Distributed systems have too many moving parts and failures can occur at various levels – hard disks can fail, the network can go down, a sudden surge in customer traffic can overload a fu...
Bitcoins are a digital cryptocurrency and have been around since 2009. As a substitute for legal tender, they are becoming the rage for investors and others but because there is no government agency auditing or performing regulatory oversights, you wonder if it is the perfect breeding ground for electronic nanocrime. Since the introduction of the Bitcoin, some competitors have emerged and the whole segment of cryptocurrencies are defined as Altcoins. Altcoins include Dogecoin, Ethereum Feathercoin, Litecoin, Novacoin, Peercoin, and Zetacoin. Some of these cryptocurrencies are considered impro...
Robotic process automation (RPA), a concept that has emerged over recent years, is still in a state of rapid evolution, existing without a clearly defined end-state or direction. As such, vendors are experimenting and pushing their products into uncharted waters - successfully or otherwise. Nonetheless, we can be sure that artificial intelligence and machine learning will continue to develop and impact on automation solutions as whole, even if at the moment these capabilities do not frequently exist within the RPA space.
In a recent post, titled “10 Surprising Facts About Cloud Computing and What It Really Is”, Zac Johnson highlighted some interesting facts about cloud computing in the SMB marketplace: Cloud Computing is up to 40 times more cost-effective for an SMB, compared to running its own IT system. 94% of SMBs have experienced security benefits in the cloud that they didn’t have with their on-premises service
In this article, we'll cover how you can monitor an application that runs on the Java Virtual Machine by going over some of the critical metrics you need to track. And, as a monitoring tool, we'll use Stackify Retrace, a full APM solution. The application we'll monitor to exemplify these metrics is a real-world Java web application built using the Spring framework. Users can register, login, connect their Reddit account and schedule their posts to Reddit.
The goal of Microservices is to improve software delivery speed and increase system safety as scale increases. Microservices being modular these are faster to change and enables an evolutionary architecture where systems can change, as the business needs change. Microservices can scale elastically and by being service oriented can enable APIs natively. Microservices also reduce implementation and release cycle time and enables continuous delivery. This paper provides a logical overview of the Microservices Reference Architecture that highlights various sub systems needed to support Microservic...
While walking around the office I happened upon a relatively new employee dragging emails from his inbox into folders. I asked why and was told, “I’m just answering emails and getting stuff off my desk.” An empty inbox may be emotionally satisfying to look at, but in practice, you should never do it. Here’s why. I recently wrote a piece arguing that from a mathematical perspective, Messy Desks Are Perfectly Optimized. While it validated the genius of my friends with messy desks, it also generated a barrage of good-natured ribbing from my super-neat friends. Emotions aside, the math is the m...
The enterprise data storage marketplace is poised to become a battlefield. No longer the quiet backwater of cloud computing services, the focus of this global transition is now going from compute to storage. An overview of recent storage market history is needed to understand why this transition is important. Before 2007 and the birth of the cloud computing market we are witnessing today, the on-premise model hosted in large local data centers dominated enterprise storage. Key marketplace players were EMC (before the Dell acquisition), NetApp, IBM, HP (before they became HPE) and Hitachi. Co...