Saif Ur Rehman Malik

A taxonomy and survey on Green Data Center Networks

Abstract Data centers are growing exponentially (in number and size) to accommodate the escalating user and application demands. Likewise, the concerns about the environmental impacts, energy needs, and electricity cost of data centers are also growing. Network infrastructure being the communication backbone of the data center plays a pivotal role in the data center’s scalability, performance, energy consumption, and cost. Research community is endeavoring hard to overcome the challenges faced by the legacy Data Center Networks (DCNs). Serious efforts have been made to handle the problems in various DCN areas. This survey presents significant insights to the state-of-the-art research conducted pertaining to the DCN domain along with a detailed discussion of the energy efficiency aspects of the DCNs. The authors explored: (a) DCN architectures (electrical, optical, and hybrid), (b) network traffic management and characterization, (c) DCN performance monitoring, (d) network-aware resource allocation, (e) DCN experimentation techniques, and (f) energy efficiency. The survey presents an overview of the ongoing research in the broad domain of DCNs and highlights the challenges faced by the DCN research community.

A survey on resource allocation in high performance distributed computing systems

Classification of high performance computing (HPC) systems is provided.Current HPC paradigms and industrial application suites are discussed.State of the art in HPC resource allocation is reported.Hardware and software solutions are discussed for optimized HPC systems. An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

Modeling and Analysis of State-of-the-art VM-based Cloud Management Platforms

Virtualization is a key aspect to achieve scalability and flexibility in a cloud. Many solutions have been proposed to monitor and deploy Virtual Machines (VM) in resource pool of cloud. However, most of the cloud management systems, such as Amazon EC2 are proprietary. In the said perspective, many open source VM-based platforms have tossed for general users to research. The existing work has mainly focused on the discussion of architecture, feature-set, and performance analysis. Other important aspects, such as formal analysis, modeling, and verification are usually ignored. In this paper, we provide formal analysis, modeling, and verification of three open source state-of-the-art VM-based cloud platforms: (a) Eucalyptus, (b) Open Nebula, and (c) Nimbus. We used High-Level Petri Nets (HLPN) to model and analyze the structural and behavioral properties of the systems. Moreover, to verify the models, we have used Satisfiability Modulo Theories Library (SMT-Lib) and Z3 Solver. We modeled about 100 VM to verify the correctness and feasibility of our models. The results reveal that the models are functioning correctly. Moreover, the increase in the number of VM does not affect the working of the models that indicates the practicability of the models in a highly scalable and flexible environment.

Trends and challenges in cloud datacenters

Next-generation datacenters (DCs) built on virtualization technologies are pivotal to the effective implementation of the cloud computing paradigm. To deliver the necessary services and quality of service, cloud DCs face major reliability and robustness challenges.

The impact of test case reduction and prioritization on software testing effectiveness

Software testing is critical but most expensive phase of Software Development Life Cycle (SDLC). Development organizations desire to thoroughly test the software. But this exhaustive testing is impractical due to resource constraints. A large number of test suites are generated using automated tools. But the real challenge is the selection of subset of test cases and/or high order test cases crucial to validate the System Under Test (SUT). Test case reduction and prioritization techniques help test manager to solve this problem at a little cost. In this paper, we investigate their impact on testing process effectiveness using previous empirical studies. The results indicate that these techniques improve the testing effectiveness significantly. At the end, a case study is presented that suggests different useful combinations of these techniques, which are helpful for different testing scenarios.

Performance analysis of data intensive cloud systems based on data management and replication: a survey

As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Modeling and Analysis of the Thermal Properties Exhibited by Cyberphysical Data Centers

Data centers (DCs) contribute toward the prevalent application and adoption of the cloud by providing architectural and operational foundation. To perform sustainable computation and storage, a DC is equipped with tens of thousands of servers, if not more. It is worth noting that the operational cost of a DC is being dominated by the cost spent on energy consumption. In this paper, we model a DC as a cyberphysical system (CPS) to capture the thermal properties exhibited by the DC. All software aspects, such as scheduling, load balancing, and all the computations performed by the devices, are considered the “cyber” component. The supported infrastructure, such as servers and switches, are modeled as the “physical” component of the CPS. We perform detailed modeling of the thermal characteristics displayed by the major components of the CPS. Moreover, we propose a thermal-aware control strategy that uses a high-level centralized controller and a low-level centralized controller to manage and control the thermal status of the cyber components at different levels. Our proposed strategy is testified and demonstrated by executing on a real DC workload and comparing it with three existing strategies, i.e., one classical and two thermal-aware strategies. Furthermore, we also perform formal modeling, analysis, and verification of the strategies using high-level Petri nets, the Z language, the Satisfiability Modulo Theories Library (SMT-Lib), and the Z3 solver.

Dynamic task mapping for Network-on-Chip based systems

Efficiency of Network-on-Chip (NoC) based multi-processor systems largely depends on optimal placement of tasks onto processing elements (PEs). Although number of task mapping heuristics have been proposed in literature, selecting best technique for a given environment remains a challenging problem. Keeping in view the fact that comparisons in original study of each heuristic may have been conducted using different assumptions, environment, and models. In this study, we have conducted a detailed quantitative analysis of selected dynamic task mapping heuristics under same set of assumptions, similar environment, and system models. Comparisons are conducted with varying network load, number of tasks, and network size for constantly running applications. Moreover, we propose an extension to communication-aware packing based nearest neighbor (CPNN) algorithm that attempts to reduce communication overhead among the interdependent tasks. Furthermore, we have conducted formal verification and modeling of proposed technique using high level Petri nets. The experimental results indicate that proposed mapping algorithm reduces communication cost, average hop count, and end-to-end latency as compared to CPNN especially for large mesh NoCs. Moreover, proposed scheme achieves up to 6% energy savings for smaller mesh NoCs. Further, results of formal modeling indicate that proposed model is workable and operates according to specifications.

DaSCE: Data Security for Cloud Environment with Semi-Trusted Third Party

Off-site data storage is an application of cloud that relieves the customers from focusing on data storage system. However, outsourcing data to a third-party administrative control entails serious security concerns. Data leakage may occur due to attacks by other users and machines in the cloud. Wholesale of data by cloud service provider is yet another problem that is faced in the cloud environment. Consequently, high-level of security measures is required. In this paper, we propose data security for cloud environment with semi-trusted third party (DaSCE), a data security system that provides (a) key management (b) access control, and (c) file assured deletion. The DaSCE utilizes Shamirs (k, n) threshold scheme to manage the keys, where k out of n shares are required to generate the key. We use multiple key managers, each hosting one share of key. Multiple key managers avoid single point of failure for the cryptographic keys. We (a) implement a working prototype of DaSCE and evaluate its performance based on the time consumed during various operations, (b) formally model and analyze the working of DaSCE using high level petri nets (HLPN), and (c) verify the working of DaSCE using satisfiability modulo theories library (SMT-Lib) and Z3 solver. The results reveal that DaSCE can be effectively used for security of outsourced data by employing key management, access control, and file assured deletion.

A Cross Tenant Access Control (CTAC) Model for Cloud Computing: Formal Specification and Verification

Sharing of resources on the cloud can be achieved on a large scale, since it is cost effective and location independent. Despite the hype surrounding cloud computing, organizations are still reluctant to deploy their businesses in the cloud computing environment due to concerns in secure resource sharing. In this paper, we propose a cloud resource mediation service offered by cloud service providers, which plays the role of trusted third party among its different tenants. This paper formally specifies the resource sharing mechanism between two different tenants in the presence of our proposed cloud resource mediation service. The correctness of permission activation and delegation mechanism among different tenants using four distinct algorithms (activation, delegation, forward revocation, and backward revocation) is also demonstrated using formal verification. The performance analysis suggests that the sharing of resources can be performed securely and efficiently across different tenants of the cloud.