A Market-Oriented Grid Directory Service for Publication and Discovery of Grid Service Providers and their Services

The emergence of Grids as a platform for sharing and aggregation of distributed resources increases the need for mechanisms that allow an efficient management of resources. The Grid economy has been identified as one of the potential solutions as it helps in managing the supply and demand for resources and enables sustained sharing of resources by providing economic incentive for Grid resource providers. An economy based Grid computing environment needs to support an infrastructure that enables the creation of a marketplace for meeting of providers and consumers. This paper presents the Grid Market Directory (GMD) that serves as a registry for publication and discovery of Grid service providers and their services.     

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

In the design and planning of next-generation Internet of Things (IoT), telecommunication, and satellite communication systems, controller placement is crucial in software-defined networking (SDN). The programmability of the SDN controller is sophisticated for the centralized control system of the entire network. Nevertheless, it creates a significant loophole for the manifestation of a distributed denial of service (DDoS) attack straightforwardly. Furthermore, recently a Distributed Reflected Denial of Service (DRDoS) attack, an unusual DDoS attack, has been detected. However, minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem. Moreover, recently the high frequencies of DDoS attacks have increased dramatically. In this paper, a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed. Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence. The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99% accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.     

Cluster computing: the commodity supercomputer

The availability of high‐speed networks and increasingly powerful commodity microprocessors is making the usage of clusters, or networks, of computers an appealing vehicle for cost effective parallel computing. Clusters, built using Commodity‐Off‐The‐Shelf (COTS) hardware components as well as free, or commonly used, software, are playing a major role in redefining the concept of supercomputing. In this paper we discuss the reasons why COTS‐based clusters are becoming popular environments for running supercomputing applications. We describe the current enabling technologies and present four state‐of‐the‐art cluster‐based projects. Finally, we summarise our findings and draw a number of conclusions relating to the usefulness and likely future of cluster computing. Copyright © 1999 John Wiley & Sons, Ltd.     

PARMON: a portable and scalable monitoring system for clusters

Workstation/PC clusters have become a cost‐effective solution for high performance computing. C‐DAC's PARAM 10000 (or OpenFrame, internal code name) is a large cluster of high‐performance workstations interconnected through low‐latency and high bandwidth networks. The management and control of such a huge system is a tedious and challenging task since workstations/PCs are typically designed to work as a standalone system rather than part of a cluster. We have designed and developed a tool called PARMON that allows effective monitoring and control of large clusters. It supports the monitoring of critical system resource activities and their utilization at three different levels: entire system, node and component level. It also allows the monitoring of multiple instances of the same component; for instance, multiple processors in SMP type cluster nodes. PARMON is a portable, flexible, interactive, scalable, location‐transparent, and comprehensive environment based on client–server technology. The major components of PARMON are parmon‐server—system resource activities and utilization information provider and parmon‐client—a GUI based client responsible for interacting with parmon‐server and users for data gathering in real‐time and presenting information graphically for visualization. The client is developed as a Java application and the server is developed as a multithreaded server using C and POSIX/Solaris threads since Java does not support interfaces to access system internals. PARMON is regularly used to monitor PARAM 10000 supercomputer, a cluster of 48+ Ultra‐4 workstations powered by the Solaris operating system. The recent popularity of Beowulf‐class clusters (dedicated Linux clusters) in terms of price–performance ratio has motivated us to port PARMON to Linux (accomplished by porting system dependent portions of parmon‐server). This enables management/monitoring of both Solaris and Linux‐based clusters (federated clusters) through a single user interface. Copyright © 2000 John Wiley & Sons, Ltd.     

E2R-F2N: Energy-efficient retailing using a femtolet-based fog network

Energy-efficient smart retail system design is a challenging research area. In this paper, we propose an automated retail system using a femtolet-based fog network. A femtolet is an indoor base station providing computation and storage. Femtolets in our system work as indoor base stations and maintain databases of the products located in their respective coverage areas. The femtolets switch to active or idle mode according to the user's presence in its coverage. A smart trolley is proposed for our retailing system, which guides the user to the particular product type selected by the user. The user, after entering the shopping mall, carries the smart trolley. The customer selects and purchases products using this trolley. On the basis of product purchasing, the respective databases maintained inside the femtolets are updated. An Android application for the proposed retailing is developed. We compare the power consumption and delay of the proposed retail system with the existing retail system. Simulation analyses illustrate that the proposed approach reduces power by approximately 89% and 94%, respectively, in comparison to the local cloud server–based and remote cloud server–based retail systems. Thus, we refer to the proposed system as a green retail system. The performance of the proposed system through experimental analysis is also evaluated.     

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical model for planning the deployment of SDN smart backup controllers (SBCs) to preserve service in the presence of DDoS attacks. Given a number of input parameters, our model has two distinct capabilities. First, it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions. Second, it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks. The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters. Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.     

Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm

Computational Grids and peer‐to‐peer (P2P) networks enable the sharing, selection, and aggregation of geographically distributed resources for solving large‐scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. We have proposed a computational economy framework for regulating the supply of and demand for resources and allocating them for applications based on the users' quality‐of‐service requirements. The framework requires economy‐driven deadline‐ and budget‐constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users' requirements are met. In this paper, we propose a new scheduling algorithm, called the DBC cost–time optimization scheduling algorithm, that aims not only to optimize cost, but also time when possible. The performance of the cost–time optimization scheduling algorithm has been evaluated through extensive simulation and empirical studies for deploying parameter sweep applications on global Grids. Copyright © 2005 John Wiley & Sons, Ltd.     

SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments

Software as a Service (SaaS) provides access to applications to end users over the Internet without upfront investment in infrastructure and software. To serve their customers, SaaS providers utilise resources of internal data centres or rent resources from a public Infrastructure as a Service (IaaS) provider. In-house hosting can increase administration and maintenance costs whereas renting from an IaaS provider can impact the service quality due to its variable performance. To overcome these limitations, we propose innovative admission control and scheduling algorithms for SaaS providers to effectively utilise public Cloud resources to maximize profit by minimizing cost and improving customer satisfaction level. Furthermore, we conduct an extensive evaluation study to analyse which solution suits best in which scenario to maximize SaaS provider?s profit. Simulation results show that our proposed algorithms provide substantial improvement (up to 40% cost saving) over reference ones across all ranges of variation in QoS parameters.