InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

Maximising revenue in cloud computing markets by means of economically enhanced SLA management

This paper proposes a bidirectional communication between market brokers and resource managers in Cloud Computing Markets. This communication is implemented by means of an Economically Enhanced Resource Manager (EERM), that supports the negotiation process by deciding which tasks can be allocated or not, and under which economic and technical conditions. The EERM also uses the economic information that collects from market layers to manage the resources accordingly to concrete BLOs. This paper shows several Business Policies and Rules for maximizing the revenue of a Cloud Provider that sells its services and resources in a market. Their validity is demonstrated through several experiments that shown how the application of these rules can have a positive influence in the revenue and minimize the violations of Service-Level Agreements.Preprin

Scientific progress of design research artefacts

Many existing IT applications exhibit strongly varying demand patterns for resources. Accommodating an ever increasing and highly fluctuating demand requires continuous availability of sufficient resources. To achieve this state at reasonably costs, a high degree of flexibility with respect to the given IT infrastructure is necessary. Facing this challenge the idea of Cloud computing has been gaining interest. In so-called Clouds resources such as CPU, storage and bandwidth can be bundled into a single services, which are offered to Cloud users. These services can be accessed in oblivion of the underlying IT infrastructure. This way Cloud Computing facilitates the introduction of new products and services without large investments in the IT infrastructure. Cloud Computing is a promising approach with a high impact on business models. One aspect of business models is clearly the revenue model, which defines how prices should be set to achieve predefined revenue level. The decision about accepting or denying requests has a high impact on the revenue of the provider. In this paper we analyze two approaches that support the cloud provider in its decision. We show that predefined policies allow increasing revenue compared to widely used technical models such as first-come-first-serve

Management of Cloud Infastructures: Policy-Based Revenue Optimization

Competition on global markets forces many enterprises to make use of new applications, reduce process times and at the same time cut the costs of their IT-infrastructure. To achieve this, it is necessary to maintain a high degree of flexibility with respect to the IT-infrastructure. Facing this challenge the idea of Cloud computing has been gaining interest lately. Cloud services can be accessed on demand without knowledge of the underlying infrastructure and have already succeeded in helping companies deploy products faster. Using Cloud services the New York Times managed to convert scanned images containing 11 million articles into PDF within 24 hours at a cost of merely 240 US-$. However Cloud providers will only offer their services, if they can realize sufficient benefit. To achieve this, the efficiency of Cloud infrastructure management must be increased. To this end we propose the use of concepts from revenue management and further enhancements

TOWARDS AN EFFICIENT DECISION POLICY FOR CLOUD SERVICE PROVIDERS

Cloud service providers may face the problem of how to price infrastructure services and how this pricing may impact the resource utilization. One aspect of this problem is how Cloud service providers would decide to accept or reject requests for services when the resources for offering these services become scarce. A decision support policy called Customized Bid-Price Policy (CBPP) is proposed in this paper to decide efficiently, when a large number of services or complex services can be offered over a finite time horizon. This heuristic outperforms well-known policies, if bid prices cannot be updated frequently during incoming requests and an automated update of bid prices is required to achieve more accurate decisions. Since CBPP approximates the revenue offline before the requests occur, it has a low runtime compared to other approaches during the online phase. The performance is examined via simulation and the pre-eminence of CBPP is statistically proven

PRIMJENA FTOPSIS METODE I IGARA PROTIV PRIRODE U IZBORU DOBAVLJAČA

Prvi element lanca opskrbljivanja je izbor dobavljača. Osnovni zadatak svake kompanije je da dobavljač bude partner na koji se može osloniti kompanija, koji će zajedno s kompanijom ulaziti u nova poslove te zajedno dijeliti rizike novih aktivnosti. Izgradnja partnerskih odnosa zahtijeva kompleksan pristup. Izbor dobavljača je individualni problem koji se postavlja pred svaku kompaniju. Ne postoje univerzalna pravila već se svakoj nabavci mora pristupati ponaosob.Uspostavljanje odnosa s dobavljačima je ključna aktivnost svake kompanije jer se uspostavljanjem dobrih odnosima s dobavljačima postiže izgradnja svih drugih modela upravljanja zalihama i proizvodnjom. Pri tome izbor i praćenje dobavljača kod svake kompanije mora biti prva aktivnost upravljanja lancem opskrbljivanja. Tek s uspostavljenim odnosima s dobavljačima je moguće provoditi ostale aktivnosti lanca opskrbljivanja

PREBUKIRANJE KAO NAČIN UPRAVLJANJA KAPACITETIMA U HOTELIJERSTVU

Menadžment prinosa je danas razvijena, znanstvena i stručna praksa koja se dosta široko rabi u svim uslužnim djelatnostima s ograničenim kapacitetom, omogućujući hotelskoj industriji donošenje najbitnijih poslovnih odluka, na primjer, koliko kapaciteta prodati, kojim klijentima i po kojoj cijeni. To je dinamičan i evoluirajući proces koji traži neprekidni monitoring i otklanjanje nedostataka i unapređivanje poslovanja. U tekstu autor iznosi glavne značajke i operativne tehnike menadžmenta prinosa u hotelijerstvu, s posebnim težištem na prebukiranje, sagledavajući pozitivne (dugoročno povećanje prihoda, bolja sposobnost upravljanja, itd.) i negativne (gubitak prihoda od smještaja, smanjenu lojalnost kupaca, gubitak ugleda hotela i sl.) posljedice na profitabilnost hotelskog poslovanja. Također se navode i moguća uopćenja tih modela, kao i utjecaji pojedinih čimbenika i parametara na same modele