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Criteria of reliability of computer networks and assessment quality of service

A modern computer network counts tens of millions of servers and hundreds of millions of workstations. Network technologies have found widespread use in industry and business, so network reliability has become one of the most pressing problems.
 In general, the reliability of all systems is determined by the reliability of their constituent elements. Different elements have different reliability. In many cases, reliability and reliability distributions are determined empirically.
 If we define the reliability of the system as the possibility of communication between different nodes and workstations, then the reliability of the Internet turns out to be equal to the whole. Unless your workstation is among thousands of nodes, you cannot guarantee the absolute reliability of the network, so it is clear that both definitions are completely unacceptable.
 The selection of criteria for determining the reliability and performance of information computer networks is one of the main problems of information systems

Structure connectivity and substructure connectivity of data center network

<abstract><p>The structure connectivity $ \kappa(G; H) $ and substructure connectivity $ \kappa^s(G; H) $ are important indicators to measure interconnection network's fault tolerance and reliability. The data center network, denoted by $ D_{k, n} $, have been proposed for data centers as a server-centric interconnection network structure, which can support millions of servers with high network capacity by only using commodity switches. In this paper, we obtain $ \kappa(D_{k, n}; S_m) $ and $ \kappa^s(D_{k, n}; S_m) $ when $ k\geq2 $, $ n\geq4 $ and $ 1\leq m\leq n+k-2 $. Furthermore, we obtain both $ \kappa(D_{k, n}; S_{23}) $ and $ \kappa^s(D_{k, n}; S_{23}) $ for $ k\geq8 $ and $ n\geq8 $.</p></abstract>

Guest Editorial: Security and Privacy for Cloud-Assisted Internet of Things (IoT) and Smart Grid

Cloud computing has emerged as a new technological domain in the IT industry. Currently, several organizations working in various domains, such as healthcare, finance, manufacturing, smart grid, Internet of Things (IoT), and IT, are increasingly integrating cloud computing with their traditional applications. The key idea behind the usage of cloud computing in IoT is to increase efficiency without compromising the data quality. When it comes to collecting data of thousands or millions of servers, the cloud offers scalability and reduces the computational load on each sensor. The highly configured servers in the cloud are very useful in processing and analyzing the sensors’ data. The security of such a shared infrastructure is very crucial. It is the major barrier in the adoption of cloud-based services, followed by issues regarding compliance, privacy, and legal matters. The digitalization of critical infrastructures, such as smart grids (SGs), brings advantages and opportunities for remote access and control. It enables intelligent and online monitoring of these systems, which considerably enhances cyberattacks’ vulnerability. Cyberattacks are among the most important threats to SGs. Therefore, efficient control systems should be designed that can detect and isolate cyberattacks to keep the SG reliable and secure operation. This special section aims at providing a forum to discuss the most recent advances on security and privacy in cloud-assisted IoT and SG applications.

Fault-Tolerant Secure Routing of B H n -Based Data Center Networks

As the core infrastructure of cloud computing, a large scale of the data center networks (DCNs), which consist of millions of servers with high capacity, suffer from node failure such that the reliability is deteriorated. Malicious group could inevitably compromise the quality and reliability of data; thus, how to ensure the security routing of data is an urgent practical problem. As models for large-scale DCNs, it is worth mentioning the balanced hypercube, which is well-known for its strong connectivity, regularity, and a smaller diameter. Each of which makes a balanced hypercube a trustworthy model to deal with data traffic and provides a certain degree of fault-tolerance as well. In this paper, we use the balanced hypercube as a model for the data center networks and design a reliable safety level by referring to different safety levels of related subgraph. This subgraph contains the source and destination nodes, and the shortest feasible paths are located so that the reliable transmission is achieved. Then, we get that the length of fault-tolerant safety routing of data center networks based on balanced hypercube is always no greater than the Hamming distance plus two. Experiment shows that our fault-tolerant security routing scheme is more effective in the same reliable network environment of DCNs.

An Optimistic Security Model for Improving Cyber Security Using Adaptive Algorithms to Prevent SQL Injection Attacks

Throughout today's economic and social life, the use of web-based services (such as e-commerce, online banking, and web-based communications, to name a few) has become a common habit. Countless applications operate worldwide on millions of servers, and their numbers are steadily increasing. It has become a focus of attackers and hackers for the attacks because of the huge growth of internet usage. It is necessary for all companies to develop and protect their applications in order to maintain their credibility and keep their products relevant for users. Web applications have brought in new classes of computer security vulnerabilities, such as SQL injection (SQLIA), and it has exceeded previously prominent vulnerability classes in recent years. SQL injection is the instance of the broader class of vulnerabilities that are based on input validation. The primary purpose of this research is to study the vulnerabilities of SQL injection and to propose an optimistic security model for secure data transmission. In this work, the authors proposed an adaptive algorithm to prevent SQL injections.

Fractional Matching Preclusion for Data Center Networks

An edge subset [Formula: see text] of [Formula: see text] is a fractional matching preclusion set (FMP set for short) if [Formula: see text] has no fractional perfect matchings. The fractional matching preclusion number (FMP number for short) of [Formula: see text], denoted by [Formula: see text], is the minimum size of FMP sets of [Formula: see text]. A set [Formula: see text] of edges and vertices of [Formula: see text] is a fractional strong matching preclusion set (FSMP set for short) if [Formula: see text] has no fractional perfect matchings. The fractional strong matching preclusion number (FSMP number for short) of [Formula: see text], denoted by [Formula: see text], is the minimum size of FSMP sets of [Formula: see text]. Data center networks have been proposed for data centers as a server-centric interconnection network structure, which can support millions of servers with high network capacity by only using commodity switches. In this paper, we obtain the FMP number and the FSMP number for data center networks [Formula: see text], and show that [Formula: see text] for [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text], [Formula: see text]. In addition, all the optimal fractional strong matching preclusion sets of these graphs are categorized.

Variable Expanding Structure for Data Center Interconnection Networks

Data centers are fundamental facilities that support high-performance computing and large-scale data processing. To guarantee that a data center can provide excellent properties of expanding and routing, the interconnection network of a data center should be designed elaborately. Herein, we propose a novel structure for the interconnection network of data centers that can be expanded with a variable coefficient, also known as a variable expanding structure (VES). A VES is designed in a hierarchical manner and built iteratively. A VES can include hundreds of thousands and millions of servers with only a few layers. Meanwhile, a VES has an extremely short diameter, which implies better performance on routing between every pair of servers. Furthermore, we design an address space for the servers and switches in a VES. In addition, we propose a construction algorithm and routing algorithm associated with the address space. The results and analysis of simulations verify that the expanding rate of a VES depends on three factors:n,m, andkwhere thenis the number of ports on a switch, themis the expanding speed and thekis the number of layers. However, the factormyields the optimal effect. Hence, a VES can be designed with factormto achieve the expected expanding rate and server scale based on the initial planning objectives.

A note on the strong matching preclusion problem for data center networks

The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. The class of data center networks has been proposed as a good class of models in the design of computer networks. Such a data center network can be viewed as a server-centric interconnection network structure, which can support millions of servers with high network capacity that uses only commodity switches. In this paper, we determine the strong matching preclusion number of k-dimensional data center networks with n-port switches and tk,n servers for k≥0 and n≥2. In addition, optimal strong matching preclusion sets of these graphs are categorized.

Fractional Matching Preclusion for Data Center Networks

An edge subset [Formula: see text] of [Formula: see text] is a fractional matching preclusion set (FMP set for short) if [Formula: see text] has no fractional perfect matchings. The fractional matching preclusion number (FMP number for short) of [Formula: see text], denoted by [Formula: see text], is the minimum size of FMP sets of [Formula: see text]. A set [Formula: see text] of edges and vertices of [Formula: see text] is a fractional strong matching preclusion set (FSMP set for short) if [Formula: see text] has no fractional perfect matchings. The fractional strong matching preclusion number (FSMP number for short) of [Formula: see text], denoted by [Formula: see text], is the minimum size of FSMP sets of [Formula: see text]. Data center networks have been proposed for data centers as a server-centric interconnection network structure, which can support millions of servers with high network capacity by only using commodity switches. In this paper, we obtain the FMP number and the FSMP number for data center networks [Formula: see text], and show that [Formula: see text] for [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text], [Formula: see text]. In addition, all the optimal fractional strong matching preclusion sets of these graphs are categorized.

Imperfect forward secrecy

We investigate the security of Diffie-Hellman key exchange as used in popular Internet protocols and find it to be less secure than widely believed. First, we present Logjam, a novel flaw in TLS that lets a man-in-the-middle downgrade connections to "export-grade" Diffie-Hellman. To carry out this attack, we implement the number field sieve discrete logarithm algorithm. After a week-long precomputation for a specified 512-bit group, we can compute arbitrary discrete logarithms in that group in about a minute. We find that 82% of vulnerable servers use a single 512-bit group, and that 8.4% of Alexa Top Million HTTPS sites are vulnerable to the attack. In response, major browsers have changed to reject short groups. We go on to consider Diffie-Hellman with 768- and 1024-bit groups. We estimate that even in the 1024-bit case, the computations are plausible given nation-state resources. A small number of fixed or standardized groups are used by millions of servers; performing precomputation for a single 1024-bit group would allow passive eavesdropping on 18% of popular HTTPS sites, and a second group would allow decryption of traffic to 66% of IPsec VPNs and 26% of SSH servers. A close reading of published NSA leaks shows that the agency's attacks on VPNs are consistent with having achieved such a break. We conclude that moving to stronger key exchange methods should be a priority for the Internet community.

Resource Request Based Energy Efficient Heuristic for Server Offloading in Cloud Computing Environment

Increasing demand for high computing power led to the establishment of large-scale data centers. A data center is a collection of millions of servers. These large-scale data centers consume a huge amount of electrical energy. Managing these servers for provisioning and de-provisioning of resources in an automatic and efficient way is a great challenge. We attempt to minimise the power consumption by reducing the number of servers and maximizing the resource utilization of the servers that are in use through virtualization of resources. We work upon Virtual Machine Consolidation for consolidating them on fewer servers for maximizing resource utilization. In this article, we propose a resource request based heuristic for offloading the overloaded servers to optimise power consumption efficiency.

LaCoDa: Layered connected topology for massive data centers

One of the fundamental challenges of existing data centers is to design a network that interconnects massive number of servers, and therefore providing an efficient and fault-tolerant routing service to upper-layer applications. Several solutions have been proposed (e.g. FatTree, DCell and BCube), however they either scale too fast (i.e., double exponentially) or too slow. This paper proposes a new data center topology, called LaCoDa, that combines the advantages of previous topologies while avoiding their limitations. LaCoDa uses a small node degree that matches physical restriction for servers, and it also interconnects a large number of servers while reducing the wiring complexity and without affecting the existing servers. LaCoDa can interconnect up to millions of servers with a small number of port switches by increasing the number of directly connected clusters per layer, and therefore avoiding the cluster connections repetitions. The theoretical and experimental results show that the average path length (APL) of a 1000-node LaCoDa is smaller than the APL of a 23-node FlatTree and 11-node BCube and DCell. Moreover, with 6-port switches and a node degree of 4, the bisection bandwidth of 4.19 Million-node LaCoDa is 7% and 14% bigger than the bisection bandwidth of 310-node DCell and BCube, respectively. Consequently, LaCoDa provides better performance than DCel, BCube, FatTree in terms of average path length, throughput, latency and bisection bandwidth.

Simple, efficient location‐based routing for data center network using IP address hierarchy

SummaryIncreasing demand and sophistication of applications deployed on data centers resulted in various designs for data center networks (DCNs). One of the major challenges in the design of DCNs is the design of routing protocol that scales to support millions of servers that a typical DCN hosts. Many alternative routing protocols are proposed to overcome the scalability problem of conventional routing protocols such as Open Shortest Path First and Routing Information Protocol. These alternative protocols that use topology characteristics of DCN are broadly classified as source routing and location‐based routing. In the process of fixing the scalability problem, these protocols introduced additional complexities such as large network control overhead and reprogramming of network elements. The extra control overhead in these protocols is the result of their effort to determine the relative location of the end hosts in a given topology. Further, existing location‐based routing is not entirely location based and covers only the latter half of a route. In our work, we present a new location‐based routing based on IP address hierarchy that (a) does not need any additional network control plane and management planes, (b) deployable on proven network technologies, and (c) covers entire path of the route. We establish the correlation between topology design and address assignments that helps determining the location of an end host directly from the address assigned to it. We demonstrate our proposed location‐based routing on an existing proven architecture for DCN, BCube‐IP and on our proposed architecture 4‐4, 1‐4. We give proper justification for proposing 4‐4, 1‐4, a better design for our proposed location‐based routing. Copyright © 2016 John Wiley & Sons, Ltd.

An efficient algorithm to construct disjoint path covers of DCell networks

Data center networks have been becoming more and more important with the development of cloud computing. For any two integers k≥0 and n≥2, the k-dimensional DCell with n-port switches, Dk,n, has been proposed for one of the most important data center networks as a server centric data center network structure. Dk,n can support millions of servers with outstanding network capacity and provide good fault tolerance by only using commodity switches. A disjoint path cover has significant applications in data center networks. In this paper, we prove that Dk,n is one-to-one r-disjoint path coverable for any integer 1≤r≤n+k−1, except for D1,2. Moreover, we propose an O(tk) algorithm for finding a one-to-one r-disjoint path cover in Dk,n for any integer 1≤r≤n+k−1, where tk is the number of servers in Dk,n.

Hamiltonian Properties of DCell Networks

DCell has been proposed for data centers as a server centric interconnection network structure. DCell can support millions of servers with high network capacity by only using commodity switches. With one exception, we prove that a $k$ level DCell built with $n$ port switches is Hamiltonian-connected for $k \geq 0$ and $n \geq 2$. Our proof extends to all generalized DCell connection rules for $n\ge 3$. Then, we propose an $O(t_k)$ algorithm for finding a Hamiltonian path in $DCell_{k}$, where $t_k$ is the number of servers in $DCell_{k}$. What's more, we prove that $DCell_{k}$ is $(n+k-4)$-fault Hamiltonian-connected and $(n+k-3)$-fault Hamiltonian. In addition, we show that a partial DCell is Hamiltonian connected if it conforms to a few practical restrictions.

ANTELOPE: A Semantic-Aware Data Cube Scheme for Cloud Data Center Networks

Today’s cloud data centers contain more than millions of servers and offer high bandwidth. A fundamental problem is how to significantly improve the large-scale system’s scalability to interconnect a large number of servers and meanwhile support various online services in cloud computing. One way is to deal with the challenge of potential mismatching between the network architecture and the data placement. To address this challenge, we present ANTELOPE, a scalable distributed data-centric scheme in cloud data centers, in which we systematically take into account both the property of network architecture and the optimization of data placement. The basic idea behind ANTELOPE is to leverage precomputation based data cube to support online cloud services. Since the construction of data cube suffers from the high costs of full materialization, we use a semantic-aware partial materialization solution to significantly reduce the operation and space overheads. Extensive experiments on real system implementations demonstrate the efficacy and efficiency of our proposed scheme.

Energy-Efficiency Classifying Mechanism for Green IT Framework of Big Data

Compared with the distributed processing, large data center focuses on information and communication tasks and has already made huge improvement on energy efficiency. But large data center includes tens of millions of servers, and its energy consumption can be ever larger than one small town. Huge energy consumption and tons of greenhouse gas emission bring many challenges to large data center in terms of energy efficiency and emission reduction. Therefore, the establishment of green and efficient large data center is imperative. This paper provides a framework of green IT for large data, focusing on energy efficiency classification and proposes an energy efficiency classification mechanism based on measurement model. According to the workload and energy consumption, this paper classifies the equipment and services seamlessly into different resource pool. Power efficiency, data center efficiency and carbon dioxide emissions are calculated and measured. This paper also formulates energy efficiency standards which could be realized and followed. The standards make the drawing of the big data center carbon trace possible and provide scheme for energy efficiency evaluation.

Energy-oriented denial of service attacks: an emerging menace for large cloud infrastructures

This work analyzes a new and very subtle kind of security threat that can affect large-scale cloud-based IT service infrastructures, by exploiting the computational resources of their component data center to waste as much energy as possible. The consequence of these threats ranges from increased costs in the energy bill, to penalization for exceeding the agreed quantity of greenhouse gases (GHG) emissions, up to complete denial of service caused by electrical outages due to power budget exhaustion. We analyzed different types of such attacks with their potential impacts on the energy consumption, modeled their behavior and quantified how current energy-proportional technologies may provide attackers with great opportunities for raising the target facility emissions and costs. These efforts resulted in a simple model with some parametric reference values that can be used to estimate the impact of such attacks also in presence of very large infrastructures containing thousands or millions of servers.

Energy saving in data processing and communication systems.

The power management of ICT systems, that is, data processing (Dp) and telecommunication (Tlc) systems, is becoming a relevant problem in economical terms. Dp systems totalize millions of servers and associated subsystems (processors, monitors, storage devices, etc.) all over the world that need to be electrically powered. Dp systems are also used in the government of Tlc systems, which, besides requiring Dp electrical power, also require Tlc-specific power, both for mobile networks (with their cell-phone towers and associated subsystems: base stations, subscriber stations, switching nodes, etc.) and for wired networks (with their routers, gateways, switches, etc.). ICT research is thus expected to investigate into methods to reduce Dp- and Tlc-specific power consumption. However, saving power may turn into waste of performance, in other words, into waste of ICT quality of service (QoS). This paper investigates the Dp and Tlc power management policies that look at compromises between power saving and QoS.

Power Management of Server Farms

The power management of server farms (Sf) is becoming a relevant problem in economical terms. Server farms totalize millions of servers all over the world that need to be electrically powered. Research is thus expected to investigate into methods to reduce Sf power consumption. However, saving power may turn into waste of performance (high response_times), in other words, into waste of Sf Quality of Service (QoS). By use of a Sfmodel, this paper investigates Sf power management strategies that look at compromises between power-saving and QoS. Various optimizing Sf power management policies are studied combined with the effects of job queueing disciplines. The (policy, discipline) pairs, or strategies, that optimize the Sf power consumption (minimum absorbed Watts), the Sf performance (minimum response_time), and the Sf performance-per-Watt (minimum response_time-per-Watt) are identified.By use of the model, the work the server-manager has to do to direct hisSf is greatly simplified, since the universe of all possible (strategies he needs to choose from is drastically reduced to a very small set of most significant strategies.