Asad Waqar Malik

Optimistic Synchronization of Parallel Simulations in Cloud Computing Environments

Cloud computing offers the potential to make parallel discrete event simulation capabilities more widely accessible to users who are not experts in this technology and do not have ready access to high performance computing equipment. Services hosted within the “cloud” can potentially incur processing delays due to load sharing among other active services, and can cause optimistic simulation protocols to perform poorly. This paper proposes a mechanism termed the Time Warp Straggler Message Identification Protocol (TW-SMIP) to address optimistic synchronization and performance issues associated with executing parallel discrete event simulation in cloud computing environments.

Can a DDoS Attack Meltdown My Data Center? A Simulation Study and Defense Strategies

The goal of this letter is to explore the extent to which the vulnerabilities plaguing the Internet, particularly susceptibility to distributed denial-of-service (DDoS) attacks, impact the Cloud. DDoS has been known to disrupt Cloud services, but could it do worse by permanently damaging server and switch hardware? Services are hosted in data centers with thousands of servers generating large amounts of heat. Heating, ventilation, and air-conditioning (HVAC) systems prevent server downtime due to overheating. These are remotely managed using network management protocols that are susceptible to network attacks. Recently, Cloud providers have experienced outages due to HVAC malfunctions. Our contributions include a network simulation to study the feasibility of such an attack motivated by our experiences of such a security incident in a real data center. It demonstrates how a network simulator can study the interplay of the communication and thermal properties of a network and help prevent the Cloud providers worst nightmare: meltdown of the data center as a result of a DDoS attack.

CloudNetSim++: A GUI Based Framework for Modeling and Simulation of Data Centers in OMNeT++

State-of-the-art cloud simulators in use today are limited in the number of features they provide, lack real network communication models, and do not provide extensive Graphical User Interface (GUI) to support developers and researchers to extend the behavior of the cloud environment. We propose CloudNetSim++, a comprehensive packet level simulator that enables simulation of cloud environments. CloudNetSim++ can be used to evaluate a wide spectrum of cloud components, such as processing elements, storage, networking, Service Level Agreement (SLA), scheduling algorithms, fine grained energy consumption, and VM consolidation algorithms. CloudNetSim++ offers extendibility, which means that the developers and researchers can easily incorporate own algorithms for scheduling, workload consolidation, VM migration, and SLA agreement. The simulation environment of CloudNetSim++ offers a rich GUI that provides a high level view of distributed data centers connected with various network topologies. The package also includes an energy computation module that provides a fine grained analysis of energy consumed by each component. This paper shows the flexibility and effectiveness of CloudNetSim++ through experimental results demonstrated using real-world data center workloads. Moreover, to demonstrate the correctness of CloudNetSim++, we performed formal modeling, analysis, and verification using High-level Petri Nets, Satisfiability Modulo Theories (SMT), and Z3 solver.

Mobile computing: issues and challenges

Mobile Cloud Computing (MCC) is an emerging field. Due to the wide usage of mobile devices and variety of applications, mobile cloud computing becomes a necessary part for mobile devices, due to reliability and portability as data processing and storage take place outside of the mobile. It is useful in a sense to save battery and computation power of mobile devices which is a serious issue in high power mobile devices. Mobile cloud computing provide mobile users a service where they can use cloud services on their mobiles and perform computations. As mobile Cloud computing is still in early stage of development, it is useful to build a thorough understanding about existing models and future trends. The purpose of this survey is to analyze and point out the major challenges and risk involved in the mobile cloud computing as well as present new trends in this field.

MPJ Express meets YARN:towards Java HPC on Hadoop systems

Many organizations—including academic, research, commercial institutions—have invested heavily in setting up High Performance Computing (HPC) facilities for running computational science applications. On the other hand, the Apache Hadoop software—after emerging in 2005— has become a popular, reliable, and scalable open-source framework for processing large-scale data (Big Data). Realizing the importance and significance of Big Data, an increasing number of organizations are investing in relatively cheaper Hadoop clusters for executing their mission critical data processing applications. An issue here is that system administrators at these sites might have to maintain two parallel facilities for running HPC and Hadoop computations. This, of course, is not ideal due to redundant maintenance work and poor economics. This paper attempts to bridge this gap by allowing HPC and Hadoop jobs to co-exist on a single hardware facility. We achieve this goal by exploiting YARN—Hadoop v2.0—that de-couples the computational and resource scheduling part of the Hadoop framework from HDFS. In this context, we have developed a YARN-based reference runtime system for the MPJ Express software that allows executing parallel MPI-like Java applications on Hadoop clusters. The main contribution of this paper is provide Big Data community access to MPI-like programming using MPJ Express. As an aside, this work allows parallel Java applications to perform computations on data stored in Hadoop Distributed File System (HDFS).

Data Center Modeling and Simulation Using OMNeT

Advances in cloud computing have rendered Service Oriented Architecture (SOA) eminent. In addition, SOA offers support for cloud computing solutions. Due to master worker paradigms, SOA is extensively adopted for cluster, grid and Cloud environment. The term Cloud computing is relatively new, compared to others. Cloud computing is define as; it is a pool of easily available, shared computing resources, including servers, services, storage and networks. Cloud comprises of computing servers arranged in racks and are connected with multiple tiers of switches to provide redundancy; this arrangement of equipment is termed as data centers. A single Cloud may comprise of multiple data centers connected through high speed communication links. Data centers have gained great publicity in recent years; however, the concepts of data center simulation models, communication protocols and analysis of data center traffic flow, remain relatively been less explored. It is important to understand how these systems work. Given the complexity of these systems, models and simulations are the best way to gain an insight into the workings of such systems. In this chapter, we provide a step by step tutorial for building traditional three tier data center simulation model using OMNeT++. The chapter is organized as follows: Section I presents core modeling and simulation concepts. In section II different architectures of data centers are discussed in detail. A step by step guide to modeling data center architectures in OMNeT++ is presented in Section III. Finally, Section IV concludes this chapter.

Aerial Threat Perception Architecture Using Data Mining

This paper presents a design framework based on a centralized scalable architecture for effective simulated aerial threat perception. In this framework data mining and pattern classification techniques are incorporated. This paper focuses on effective prediction by relying on the knowledge base and finding patterns for building the decision trees. This framework is flexibly designed to seamlessly integrate with other applications.

An Intelligent Opportunistic Scheduling of Home Appliances for Demand Side Management

Demand side management plays a vital role in load shifting to off peak hours from on peak hours in response to dynamic pricing. In this paper, we propose an optimal stopping rule (OSR) and firefly algorithm (FA) for the demand response based on cost minimization. Each appliance gets the best opportunistic time to start its operation in response to dynamic electricity pricing. The threshold based cost is computed for each appliance where each appliance has its own priority and duty cycle regardless of their energy consumption profile. Numerical simulations show that our proposed scheme performed well in lowering cost, waiting time and peak to average ratio.

Towards Real-Time Opportunistic Scheduling of the Home Appliances Using Evolutionary Techniques

The tremendous evolution of the technology has empowered the energy consumers to receive real-time information regarding electricity consumption prices with the help of two way communication between the main grid and the smart meter. We have proposed evolutionary optimization techniques such as; genetic algorithm (GA) and teaching-learning base algorithm (TLBO) in this paper. The aforementioned algorithms are exploited to find out an optimal schedule for every appliance based on real-time pricing (RTP) signal. It enables the real-time automation of smart home appliances considering the economic criteria of each smart home. Our scheduling strategy shifts the extra load exceeding the threshold limit to the hours where the electricity pricing is low. In this way, we can reduce electricity cost while considering the user comfort by reducing delay and peak to average ratio (PAR).

IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.